System and method for providing additonal monthly income by capitalizing expected annual tax refund to monthly payments and or predicting accurate tax withholdings for certain taxpayers

ABSTRACT

Systems and methods that may be used to provide a predictive tax loan or other monetary advance before the loan recipient (e.g., a taxpayer) prepares and files its tax return. A risk of providing a predictive tax loan or monetary advance is modeled separately from a machine learning model used to determine the anticipated tax refund amount and tax loan. The disclosed systems and methods may also predict accurate tax withholdings based on multiple machine learning models from multiple services, including non-payroll related services.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of U.S. application Ser.No. 16/860,102 filed Apr. 28, 2020. The entirety of the above-listedapplication is incorporated herein by reference.

BACKGROUND

In today's market, there are financial institutions, corporations,online and other tax preparation services that offer monetary advancesto clients and or other tax payer's based on the taxpayer's anticipatedtax refund as determined by his/her as-filed tax return. These advancesare often referred to as “tax refund loans” and are essentiallyshort-term advances on a tax refund a taxpayer expects to receive basedon his/her as-filed tax return. The loan is provided as a lump sumamount, often ranging from $200 to $3500. The loan amount is deductedfrom the taxpayer's refund by the institution, corporation, or serviceafter the refund is issued by the tax authority (e.g., Internal RevenueService (IRS), state tax authority).

The current process has some undesirable shortcomings. For example,current tax refund loans are only available after the taxpayer's taxreturn has been formally submitted and accepted by the tax authority.Many taxpayer's, however, cannot wait that long and may want the taxrefund loan early—i.e., before submitting their tax returns. Forexample, many taxpayers often live paycheck to paycheck and could usethe anticipated tax refund or portions of it to supplement his/herweekly or monthly income. Moreover, these and other taxpayers may desireadvanced monthly payments, meaning that an anticipated tax refund mustbe determined several months to a year in advance to fulfill thetaxpayer's needs. As can be appreciated, providing monthly unsecuredpayments several months to a year in advance of the taxpayer's taxreturn filing and unknown tax refund amount is a risky proposition forthe tax loan provider as the taxpayer's tax situation, and hence itsability to repay the loan, may change by the time its tax return isfiled.

Accordingly, there is a need and desire for a method of providing a taxloan or other monetary advance that may be spread out over monthlyinstallments and prior to a taxpayer's tax return filing. There is alsoa need and desire to minimize risk to the provider of the loan/advance,particularly when it is provided months before the taxpayer's tax filingand his/her actual tax refund amount is determined.

Another way for a taxpayer to have more monthly income is to betterestimate his/her paycheck tax withholdings. It is known to withholdtaxes from a taxpayer's paycheck to help offset the taxpayer's annualstate and federal tax liability. In many cases, the taxpayer gets arefund returning excess tax withholdings. Many taxpayers would preferwithholding less taxes and having more money per pay period, then havingan annual tax refund. However, the taxpayer does not know his/herexpected annual income and anticipated deductions to properly forecasthis/her tax liability. Often times, this uncertainty causes the taxwithholdings to be larger than necessary. Accordingly, there is a needand desire to properly forecast a taxpayer's paycheck tax withholdingsto minimize over-taxation and increase the taxpayer's take home pay.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows an example of a system configured to implement a processfor providing additional monthly income by providing a risk mitigatedpredictive tax loan and or predictive tax withholdings in accordancewith an embodiment of the present disclosure.

FIG. 2 shows a server device according to an embodiment of the presentdisclosure.

FIG. 3 shows a functional block diagram of an example process forproviding additional monthly income by providing a risk mitigatedpredictive tax loan and or predictive tax withholdings according to anembodiment of the present disclosure.

FIG. 4 shows an example tax loan evaluation process according to anembodiment of the present disclosure that may be used in the processillustrated in FIG. 3.

FIG. 5 shows an example tax withholdings evaluation process according toan embodiment of the present disclosure that may be used in the processillustrated in FIG. 3.

DETAILED DESCRIPTION OF SEVERAL EMBODIMENTS

Embodiments described herein may be used to provide additional monthlyincome to certain taxpayers. For example, in one embodiment, apredictive tax loan or other monetary advance is determined and may beprovided before the loan recipient (e.g., a taxpayer) prepares and filesits tax return. In one or more embodiments, the predictive tax loan maybe spread out over monthly installments. In one or more embodiments, forqualifying taxpayer's the monthly installments may be dynamicallyadjusted based on an updated evaluation of the taxpayer's situation andother related information. In one or more embodiments, the loanprovider's (e.g., tax service) risk is mitigated by determining a risklevel for the tax payer and only providing the loan for low risktaxpayer's.

In one or more embodiments, the risk of providing a predictive tax loanor monetary advance is modeled separately from a machine learning modelused to determine the anticipated tax refund amount. In one or moreembodiments, eligible taxpayers may be provided with a monthly loan-likepayment that is expected to be paid back by the future predicted annualtax refund amount. In one or more embodiments, taxpayer's that allow thedisclosed system and method to more know more about themselves, such astaxpayer's within an online community or taxpayer's that participate insurveys throughout a year may be provided with more precise initialestimates and dynamically adjusted monthly loan installments.

One or more embodiments described here may be used to provide additionalmonthly income to certain taxpayers by determining and providingpredictive tax withholdings to better estimate a taxpayer's paycheckwithholdings and mitigate over-taxation. In one or more embodiments, oneor more machine learning tax withholdings models are trained based onvarious attributes and the trained models are used to predict moreaccurate tax withholdings, providing the taxpayer with more take homepay while still meeting its anticipated tax obligations.

FIG. 1 shows an example of a system 100 configured to implement aprocess for providing additional monthly income by providing a riskmitigated predictive tax loan and or predictive tax withholdingsaccording to an embodiment of the present disclosure. System 100 mayinclude a first server 120, second server 140, and/or a user device 150.First server 120, second server 140, and/or user device 150 may beconfigured to communicate with one another through network 110. Forexample, communication between the elements may be facilitated by one ormore application programming interfaces (APIs). APIs of system 100 maybe proprietary and/or may be examples available to those of ordinaryskill in the art such as Amazon® Web Services (AWS) APIs or the like.Network 110 may be the Internet and/or other public or private networksor combinations thereof.

First server 120 may be configured to implement a first service 122,which in one embodiment may be used to input data suitable for trainingthe machine learning models disclosed herein and or input the data usedto determine tax loans and withholdings in accordance with the disclosedprinciples. In one or more embodiments, the data may be input vianetwork 110 from one or more databases 124, 144, the second server 140and/or user device 150. For example, first server 120 may execute therisk mitigated predictive tax loan or monetary advance process accordingto the disclosed principles using data stored in database 124, database144 and or received from second server 140 and/or user device 150. Firstservice 122 or second service 142 may implement an information service,which may maintain data used throughout the process that may provide arisk mitigated predictive tax loan and or predictive tax withholdings.The information service may be any network 110 accessible service suchas TurboTax®, QuickBooks®, QuickBooks® Payroll, Mint®, Credit Karma™,and their respective variants, offered by Intuit® of Mountain ViewCalif.

User device 150 may be any device configured to present user interfacesand receive inputs thereto. For example, user device 150 may be asmartphone, personal computer, tablet, laptop computer, or other device.

First server 120, second server 140, first database 124, second database144, and user device 150 are each depicted as single devices for ease ofillustration, but those of ordinary skill in the art will appreciatethat first server 120, second server 140, first database 124, seconddatabase 144, and/or user device 150 may be embodied in different formsfor different implementations. For example, any or each of first server120 and second server 140 may include a plurality of servers or one ormore of the first database 124 and second database 144. Alternatively,the operations performed by any or each of first server 120 and secondserver 140 may be performed on fewer (e.g., one or two) servers. Inanother example, a plurality of user devices 150 may communicate withfirst server 120 and/or second server 140. A single user may havemultiple user devices 150, and/or there may be multiple users eachhaving their own user device(s) 150.

FIG. 2 is a block diagram of an example computing device 200 that mayimplement various features and processes as described herein. Forexample, computing device 200 may function as first server 120, secondserver 140, or a portion or combination thereof in some embodiments. Thecomputing device 200 may be implemented on any electronic device thatruns software applications derived from compiled instructions, includingwithout limitation personal computers, servers, smart phones, mediaplayers, electronic tablets, game consoles, email devices, etc. In someimplementations, the computing device 200 may include one or moreprocessors 202, one or more input devices 204, one or more displaydevices 206, one or more network interfaces 208, and one or morecomputer-readable media 210. Each of these components may be coupled bya bus 212.

Display device 206 may be any known display technology, including butnot limited to display devices using Liquid Crystal Display (LCD) orLight Emitting Diode (LED) technology. Processor(s) 202 may use anyknown processor technology, including but not limited to graphicsprocessors and multi-core processors. Input device 204 may be any knowninput device technology, including but not limited to a keyboard(including a virtual keyboard), mouse, track ball, and touch-sensitivepad or display. Bus 212 may be any known internal or external bustechnology, including but not limited to ISA, EISA, PCI, PCI Express,USB, Serial ATA or FireWire. Computer-readable medium 210 may be anymedium that participates in providing instructions to processor(s) 202for execution, including without limitation, non-volatile storage media(e.g., optical disks, magnetic disks, flash drives, etc.), or volatilemedia (e.g., SDRAM, ROM, etc.).

Computer-readable medium 210 may include various instructions 214 forimplementing an operating system (e.g., Mac OS®, Windows®, Linux). Theoperating system may be multi-user, multiprocessing, multitasking,multithreading, real-time, and the like. The operating system mayperform basic tasks, including but not limited to: recognizing inputfrom input device 204; sending output to display device 206; keepingtrack of files and directories on computer-readable medium 210;controlling peripheral devices (e.g., disk drives, printers, etc.) whichcan be controlled directly or through an I/O controller; and managingtraffic on bus 212. Network communications instructions 216 mayestablish and maintain network connections (e.g., software forimplementing communication protocols, such as TCP/IP, HTTP, Ethernet,telephony, etc.).

Predictive tax loan and withholdings instructions 218 may includeinstructions that implement the disclosed additional monthly incomeprocessing described herein.

Application(s) 220 may be an application that uses or implements theprocesses described herein and/or other processes. The processes mayalso be implemented in operating system 214.

The described features may be implemented in one or more computerprograms that may be executable on a programmable system including atleast one programmable processor coupled to receive data andinstructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., Objective-C, Java), includingcompiled or interpreted languages, and it may be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructions mayinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. Generally, a processor may receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer may include a processorfor executing instructions and one or more memories for storinginstructions and data. Generally, a computer may also include, or beoperatively coupled to communicate with, one or more mass storagedevices for storing data files; such devices include magnetic disks,such as internal hard disks and removable disks; magneto-optical disks;and optical disks. Storage devices suitable for tangibly embodyingcomputer program instructions and data may include all forms ofnon-volatile memory, including by way of example semiconductor memorydevices, such as EPROM, EEPROM, and flash memory devices; magnetic diskssuch as internal hard disks and removable disks; magneto-optical disks;and CD-ROM and DVD-ROM disks. The processor and the memory may besupplemented by, or incorporated in, ASICs (application-specificintegrated circuits).

To provide for interaction with a user, the features may be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.

The features may be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combinationthereof. The components of the system may be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a telephone network, aLAN, a WAN, and the computers and networks forming the Internet.

The computer system may include clients and servers. A client and servermay generally be remote from each other and may typically interactthrough a network. The relationship of client and server may arise byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

One or more features or steps of the disclosed embodiments may beimplemented using an API. An API may define one or more parameters thatare passed between a calling application and other software code (e.g.,an operating system, library routine, function) that provides a service,that provides data, or that performs an operation or a computation.

The API may be implemented as one or more calls in program code thatsend or receive one or more parameters through a parameter list or otherstructure based on a call convention defined in an API specificationdocument. A parameter may be a constant, a key, a data structure, anobject, an object class, a variable, a data type, a pointer, an array, alist, or another call. API calls and parameters may be implemented inany programming language. The programming language may define thevocabulary and calling convention that a programmer will employ toaccess functions supporting the API.

In some implementations, an API call may report to an application thecapabilities of a device running the application, such as inputcapability, output capability, processing capability, power capability,communications capability, etc.

FIG. 3 illustrates a functional block diagram of an example process 300for providing additional monthly income by providing a risk mitigatedpredictive tax loan and or predictive tax withholdings according to anembodiment of the present disclosure. Although the disclosed principlesare described as providing “monthly” income, it should be appreciatedthat the disclosed principles may provide additional income on anyperiodic (weekly, bi-weekly, etc.) basis. In addition, the tax loanfeatures of the disclosed embodiments may be used to issue an aperiodic,lump sum payment if desired. Accordingly, while the process 300 is nowdescribed as providing monthly relief to a taxpayer, it should beappreciated that the process 300 is not so limited and may provideadditional periodic or aperiodic income to certain taxpayers inaccordance with the disclosed principles.

The process 300 may include a tax loan evaluation process 320 and a taxwithholdings evaluation process 330. In one or more embodiments, the taxloan evaluation process 320 may train and use machine learning modelssuch as a tax refund model 302 and a separate taxpayer risk model 304.In one or more embodiments, the tax loan evaluation process 320 maydetermine and provide a tax loan 322 for low risk taxpayers as discussedbelow in more detail. In addition, the tax loan may be provided inmonthly installments, which may be dynamically adjusted for certaintaxpayers.

In one or more embodiments, the tax withholdings evaluation process 330may train and use one or more machine learning tax withholding models306. In one or more embodiments, the tax withholdings evaluation process330 may provide predictive tax withholdings that may be stored withpayroll data 312 used by a payroll service to deduct the predictedamount of tax withholdings from the taxpayer's paycheck. In one or moreembodiments, the tax withholdings evaluation process 330 may useinformation from services related to the payroll service to providebetter tax withholding estimates for certain taxpayers. For example, ifthe taxpayer's employer uses a payroll service such as QuickBooks®Payroll, and a taxpayer uses related services such as TurboTax®,QuickBooks®, Mint®, Credit Karma™, and or their respective variants,models and information from the related services may be used by the taxwithholdings evaluation process 330 to better estimate the taxwithholdings deducted by the payroll service. Accordingly, each relatedservice may have its own machine learning withholding model 306 that maybe trained and used in accordance with the disclosed principles.

The disclosed models 302, 304, 306 may have access to one or moredatasets 312 having data necessary to train and use the models 302, 304,306 in accordance with the tax loan evaluation process 320 and taxwithholdings evaluation process 330. The tax withholdings evaluationprocess 330 and its corresponding models 306 may use and or updatepayroll data 312 for certain taxpayers as discussed herein.

In one embodiment, system 100 may perform some or all of the processingillustrated in FIG. 3. For example, first server 120 may perform the taxloan evaluation process 320 and tax withholdings evaluation process 330discussed in detail below with respect to FIGS. 4-5. Moreover, duringthe execution of the processes 320, 330, first server 120 may input thedataset(s) 310 and payroll data 312 from a database, which may be thefirst database 124 and or second database 144, and or the second server140 and/or user device 150.

FIG. 4 illustrates an example tax loan evaluation process 320 that maybe used in the process 300 illustrated in FIG. 3. As discussed in moredetail below, the process 320 may separately profile two aspects for thepotential tax loan or monetary advance: 1) predicted refund amount; and2) risk associated with providing the loan to the particular taxpayer.To that end, two separate machine learning models may be used, a taxrefund model (e.g., model 302) and a taxpayer risk model (e.g., model304). The two models may input data from the same or different datasetsand may be trained in accordance with the disclosed principles. In oneor more embodiments, the data may be related to the taxpayers' industry,workplace stability, historical refund amounts, to name a few.Demographic information (e.g., age, marital status, income, education,and employment) may also be used during the process 320. Moreover, asdiscussed further bellow, the process 320 may be able to dynamicallyadjust monthly payments of a predicted loan amount for certain taxpayersthat provide additional or updated data throughout the year.

At step 402, the process 320 may input data from a database such ase.g., an online community database associated with a plurality oftaxpayers. For example, if the process 320 is being performed as part ofthe TurboTax® or TurboTax® Live service, then the input dataset (e.g.,dataset 310) may come from a TurboTax® and or TurboTax® Live communitydatabase, which may have data for millions of taxpayers. In one or moreembodiments, data is input and grouped for each past taxpayer within thecommunity (e.g., TurboTax® user) dating back at least two years. Itshould be appreciated that the further back the data goes, the moreaccurate the process 320 may become. Accordingly, the disclosedprinciples are not limited to two years' worth of data.

At step 402, the process 320 may also label the input data for use withthe tax refund and taxpayer risk models. In one or more embodiments, themost recent year's data may be used for labelling the input data.Labelled attributes may include minimum refund amount, maximum refundamount, median refund amount, mean refund amount, standard error of therefund mean, industry code, credit card score, number of jobs switchedin the past five years, to name a few. It should be appreciated thatthese are examples of labels that may be used and that the disclosedprinciples are not limited to the illustrated examples.

At step 404, the process 320 may train the tax refund model (e.g., model302) using the labeled input data from step 402. In one or moreembodiments, the tax refund model may be a regression model using theprior year's refund data as labels. The model may be trained by passingthe labeled data through the model. A regression model may be used forregression analysis to estimate the relationships between a dependentvariable (often called the “outcome variable”) and one or moreindependent variables (often called “predictors” or “features”). Typesof regression models that may be used for the tax refund model include,but are not limited to, linear regression, polynomial regression,quantile regression, lasso regression, elastic net regression, orprincipal components regression (PRC), to name a few.

At step 406, the process 320 may train the tax risk model (e.g., model304) using the labeled input data from step 402. In one or moreembodiments, the tax refund model may be a classification model. Themodel may be trained by passing the labeled data through the model. Aclassification model attempts to draw one or more conclusions from theinput values given to it for training. A classification model output isoften a probability number for the dataset typically between 0 and 1.Types of classification models that may be used for the tax risk modelinclude, but are not limited to, logistic regression, Naïve Bayes,stochastic gradient descent, K-nearest neighbors, decision tree, randomforest, support vector machine (SVM), xgboost, and convolutional neuralnetwork (CNN), to name a few.

At 408, the process 320 may predict a tax refund amount for a particulartaxpayer using the trained tax refund model (e.g., e.g., model 304). Inone or more embodiments, the predicted tax refund amount may be used asthe basis for the taxpayer's tax loan. In one or more embodiments, thepredicted refund amount and hence the loan amount may be a refund amountwith a confidence interval over a predetermined threshold. For example,the predetermined threshold may be a 90% confidence level as determinedby the tax refund model. In one or more embodiments, a lowest estimatedrefund amount with a confidence level above the predefined threshold(e.g. 90% confidence level) may be used as the loan amount. It should beappreciated that the predefined threshold is not limited to a 90%confidence level and that the disclosed principles may use a higher orlower threshold if desired.

At 410, the process 320 may predict the risk of providing the particulartaxpayer with the tax loan using the trained risk model. In one or moreembodiments, if it is believed that there is not enough data to properlyassess the taxpayer's risk, the taxpayer's credit score may be used toassess his/her risk. The credit score may be may be one of theattributes retrieved from the input data (at step 402) or it may beretrieved from a credit bureau database via the network 110. In one ormore embodiments, regardless of the model's determination of ataxpayer's risk, a taxpayer's risk level may be set to a “high risk” ifit is determined that the taxpayer has not targeted its refund (orloaned amount) to the tax preparation service in advance—that is, ataxpayer will be a “high risk” taxpayer if he/she has elected todirectly receive all of its tax refund instead of directing the refund(minus adjustments) to the tax preparation service. Likewise, a taxpayerwill be a “high risk” taxpayer if he/she has not set up or selected anoption to automatically repay his/her potential tax loan to the taxpreparation service via an automatic deduction from its tax refund.

At 412, a taxpayer having a risk level (as determined at step 410) belowa predetermined rick threshold may be provided a loan in the refundamount determined at step 408. In one or more embodiments, a taxpayerwith a risk level below the predetermined rick threshold may be declareda “low risk” taxpayer and be afforded the opportunity to accept thepredicted tax loan. In one or more embodiments, the user may bepresented with a graphical user interface, link and or other selectionprocess to accept the tax loan.

In one or more embodiments, the tax loan may be provided in monthlyinstallments. In one or more embodiments, the number of monthlyinstallments may be determined by the process 320 (e.g., using thenumber of months between the present date and the anticipated tax refundreceipt date) and or it may be selected by the taxpayer (via an optionwhen prompted to accept the loan).

In accordance with the disclosed principles, taxpayers that provide theprocess 320 with additional data (e.g., data beyond what the taxpreparation service requires to prepare an electronic tax return) may bere-examined periodically (e.g., monthly) to adjust his/her expected taxreturn and hence his/her monthly loan installments. For example, at step414, using taxpayer information for taxpayers in which the system 100and processes 300, 320 know more about, the process 320 may update thetax refund and risks models every month using the updated information.Qualifying taxpayers may then have his/her loan and risk evaluationsupdated at the same rate.

The additional data may be input from other services related to theservice performing process 320. For example, if process 320 is beingperformed as part of the TurboTax® or TurboTax® Live tax preparationservices, then related services may include QuickBooks®, QuickBooks®Payroll, Mint®, and variants thereof. For example, a Mint® user who haschanged his/her workplace since receiving a tax loan may eventuallyreceive a different tax refund than what was predicted (at step 408)when his/her return is actually filed and or may have altered its riskscore from what was determined at step 410. That is, the anticipatedrefund amount may have increased/decreased depending upon the taxpayer'snew circumstances. Likewise, the taxpayer's ability to repay the loanmay have become less or more risky. These changes may be taken intoaccount by reevaluating the taxpayer in accordance with the disclosedprinciples. Moreover, additional information may be provided by thetaxpayers themselves by completing surveys and requests for informationfrom the tax preparation service. For these types of taxpayers, theprocess 320 may update the taxpayer's monthly installment by continuingat step 408 (and using the updated tax refund and risk models).

FIG. 5 illustrates an example tax withholdings evaluation process 330that may be used in the process 300 illustrated in FIG. 3. As notedabove, in one or more embodiments, the tax withholdings evaluationprocess 330 may use information from services related to the payrollservice to provide better tax withholding estimates for certaintaxpayers. For example, if the taxpayer's employer uses a payrollservice such as QuickBooks® Payroll, and a taxpayer uses relatedservices such as TurboTax®, QuickBooks®, Mint®, Credit Karma™, and ortheir respective variants, additional models (e.g., withholding models306) and information from the related services may be used by the taxwithholdings evaluation process 330 to better estimate the taxwithholdings to be deducted by the payroll service. Accordingly, eachrelated service may have its own machine learning withholding model 306that may be trained and used in accordance with the disclosedprinciples.

For example, at step 502, the payroll service and each related servicemay input and label data to be used for its respective model. In one ormore embodiments, the labeled attributes may vary for each service.Example labeled attributes include, but are not limited to, salaryamount, salary fluctuation, extra work shifts, overtime pay, salaryraise, second job, savings and or checking account balances, creditscore, past refund amounts, life changing events (e.g., marriage, birthof child), indications of deductible expenses, and employer related data(e.g., industry), to name a few. In addition, changes in federal and orstate tax policies may be input into one or more models or the decisionmaking processed described herein. That is, changes in state/federalcode can be embedded in the regression model by adding a component withsome business logic adjusting the output due to the change in law. Forexample, if the Florida state tax is higher this year, the disclosedprinciples can penalize all Florida residents with a decrease in theirexpected refund.

At step 504, each service's tax withholdings model may be trained usingthe relevant collected and labeled dataset. In one or more embodiments,the prediction of tax withholdings is approached as a regressionproblem. Accordingly, the models used for the tax withholdings modelsare regression models such as the ones discussed above with respect toFIG. 4. In a supervised learning framework, the labeled attributes ofthe taxpayers' financial status (as known by the relevant service) areretroactively set at the end of the year to set the “correct”withholding amount for the prior year. That is, after a tax year endsand refunds are issued, the process (at step 504) automatically“corrects” the tax withholdings backwards in the training dataset. Ascan be appreciated, this makes each model more accurate as the actualdetermined withholding amounts and factors contributing to them may beentered into the models to retrain them.

At step 506, taxpayers associated with the payroll service (e.g.,QuickBooks® Payroll) are crossed-reference with users of the relatedservices (e.g., TurboTax®, QuickBooks®, Mint®, Credit Karma™, etc.) todetermine if they are eligible to receive adjusted tax withholdings inaccordance with the tax withholdings evaluation process 330. In one ormore embodiments, the process 330 may use taxpayer name, address, phonenumber, and or social security number to find related services for thetax payer. In one or more embodiments, financial data may be crossedreferenced as well. For example, salary details issued by the payrollservice may be cross-referenced with transaction data extracted by afinancial service (e.g., Mint®), tax filing data from a tax preparationservice (TurboTax®), credit score data from a credit service (CreditKarma™), bank account data, to name a few.

Taxpayers identified with one or more of the related services may beeligible for the predictive tax withholdings disclosed herein. In one ormore embodiments, eligible taxpayers may be given an option to “opt in”to adjust his/her withholdings based on the predicted tax withholdingsdisclosed herein. The option may be provided by the payroll service viaa graphical user interface, link, emailed communication and or otherform of communication. Taxpayers that do not opt in to adjust his/herwithholdings based on the predicted tax withholdings disclosed hereinmay continue to have its withholdings based on his/her W-4 form.

At step 508, the eligible taxpayer's information is run through thetrained models and his/her tax withholdings are set based on thecollective outputs of the models. For example, when more than one modelis used, there is room to implement a policy of how to distill themultiple results into a single action/result. For example, the disclosedsystem and process can run multiple regression models and decide to usethe average or the lower range of the outputs. The disclosed principles,may also apply a more complex policy such as using the output of themodel that is most confident. In one or more embodiments, the policy mayimplement a decision-tree like policy for selecting the best output. Thepredictive tax withholdings are set in the data of the payroll serviceand used to deduct federal and or state taxes from the taxpayer'spaycheck. As can be appreciated, the disclosed process 330 may determinetighter, more accurate tax withholdings based on the numerous datapoints and trained models.

As can be appreciated, the disclosed systems and processes provideseveral advantages over conventional tax loan services and payrollservices to provide certain taxpayers with more periodic (e.g., monthly)income. For example, currently, there is not one service in the marketthat offers tax loans before a taxpayer prepares and or submits a taxreturn. By mitigating risk and predicting an accurate future tax refundamount, a taxpayer may be able to receive much need periodic incomerelief by receiving his/her tax refund when it is needed as opposed towaiting a year to receive it. Moreover, by dynamically altering theinstallments, the disclosed principles ensure that the taxpayer isreceiving payments he/she can repay when his/her refund isreceived—thus, further mitigating the risk to the loan provider.

In one or more embodiments, millions of relevant data and data pointsare analyzed and or used to train separate tax refund and tax riskmodels for determining tax loans and associated risks of providing theloans to certain taxpayers. The data may be retrieved from one or morenetworked systems, devices and or storage mediums. The data used is toomassive and diverse and cannot be processed without the distributedsystem architecture disclosed herein. By separately modeling predictedrefund amount and risk, two different types of machine learning modelsmay be used—that is, an specific model type (e.g., regression model) maybe used for determining a predicted refund, while another specific modeltype (e.g., classification) may be used for determining the risk ofproviding a particular taxpayer with the loan as refund amount and riskare two entirely different attributes requiring different forms ofanalysis.

In addition, by allowing for dynamic updating of certain taxpayers'refund amount and risk, the disclosed principles utilize non-traditionalsources of information to ensure the disclosed systems/processes areaccurately serving the taxpayers' needs as well as the loan provider's.The disclosed principles provide taxpayers the ability to stay engagedin his/her finances and tax obligations by providing the dynamicallyupdated loan installments, etc.

Moreover, by more accurately determining a taxpayer's paycheck taxwithholdings, the taxpayer may be provided with more periodic take homepay while still meeting his/her tax obligations. As with other processesdisclosed herein, the tax withholdings processing utilizes millions ofrelevant data and data points and separate financial and or tax servicesto train and use models in a retroactive manner. The data may beretrieved from one or more networked systems, devices and or storagemediums. The data used is too massive and diverse and cannot beprocessed without the distributed system architecture disclosed herein.

Moreover, the disclosed principles may determine tax withholdings thatare more precise than the conventional W-4-based tax withholdings. Inaddition, the tax withholdings process may combine two or more separatecomputer systems and services, and their respective data sources, in anon-conventional manner for estimating tax withholdings, which aretypically determined by the payroll service. The disclosed principlesutilize a non-trivial setting of supervised learning whereby the targetvariable (i.e., withholding amount) is set retrospectively and rich,non-trivial features may be taken from multiple separate systems.

As such, the disclosed processes are an advancement in the taxpreparation, tax estimation and machine learning arts as millions ofdata and data points from diverse systems may be used innon-conventional manners for specific purposes: 1) tax loans determined,provided and or adjusted before a tax return is even provided; and 2)accurate tax withholdings amounts provided based on more relevantinformation, including non-payroll information.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example and notlimitation. It will be apparent to persons skilled in the relevantart(s) that various changes in form and detail can be made thereinwithout departing from the spirit and scope. In fact, after reading theabove description, it will be apparent to one skilled in the relevantart(s) how to implement alternative embodiments. For example, othersteps may be provided, or steps may be eliminated, from the describedflows, and other components may be added to, or removed from, thedescribed systems. Accordingly, other implementations are within thescope of the following claims.

In addition, it should be understood that any figures which highlightthe functionality and advantages are presented for example purposesonly. The disclosed methodology and system are each sufficientlyflexible and configurable such that they may be utilized in ways otherthan that shown.

Although the term “at least one” may often be used in the specification,claims and drawings, the terms “a”, “an”, “the”, “said”, etc. alsosignify “at least one” or “the at least one” in the specification,claims and drawings.

Finally, it is the applicant's intent that only claims that include theexpress language “means for” or “step for” be interpreted under 35U.S.C. 112(f). Claims that do not expressly include the phrase “meansfor” or “step for” are not to be interpreted under 35 U.S.C. 112(f).

What is claimed is:
 1. A computer implemented method for providingadditional periodic income to a taxpayer, the method provided by a taxpreparation service to a community of taxpayers who have used the taxpreparation service in the past to prepare tax returns for thetaxpayers, said method comprising: receiving, from a mobile computingdevice associated with the taxpayer, a request for additional periodicincome, wherein the income comprises a tax refund loan paid by the taxpreparation service to the taxpayer in installments, wherein the requestis received via an interface on the mobile computing device generated byan application executing on the mobile computing device; inputting, inresponse to receiving the request, by a server, a first set of dataassociated with the taxpayer into a trained regression model forpredicting a tax refund amount for the taxpayer without the taxpreparation service or the taxpayer preparing a tax return, the trainedregression model outputting the predicted tax refund, wherein thetrained regression model is trained using a supervised learningframework on data retrieved from a community database, the retrieveddata being different from the first set of data; inputting, in responseto receiving the request, by the server, the first set of dataassociated with the taxpayer into a trained classification model fordetermining a risk of providing a tax loan to the taxpayer, the trainedclassification model being of a different type of model than a type ofthe trained regression model, the trained classification modeloutputting the determined risk of providing the tax loan to thetaxpayer, wherein the trained classification model is trained using asupervised learning framework on the data retrieved from the communitydatabase; determining whether the taxpayer may be provided the taxrefund loan in an amount of the predicted tax refund based on thedetermined risk, wherein the risk is based at least in part on whetherthe taxpayer uses the tax preparation service to prepare the current taxreturn; receiving, from the mobile computing device via the interface,by the server, a request by the taxpayer to opt in for providingadditional withholding data to the tax preparation service, wherein theadditional withholding data is provided by services related to butseparate from the tax preparation service and comprises payroll, creditscore, and accounting data associated with the taxpayer; in response tothe taxpayer opting in for providing the additional withholding data:inputting, by the server, a first set of additional withholding dataassociated with the taxpayer into a second trained regression machinelearning model for predicting a tax withholding amount for each payperiod for the taxpayer, wherein the second trained regression model istrained using a supervised learning framework on the data retrieved formthe community database; determining, based on the output of the models,that the taxpayer may be provided with a tax refund loan in a definedamount to be paid to the taxpayer on an installment basis; in responseto determining that the taxpayer may be provided with the tax loan,presenting a selection graphical object, in a user interface, for thetaxpayer to accept the loan; in response to receiving a selection of thetaxpayer accepting the loan, generating an additional periodic incomefor the taxpayer by: providing the taxpayer with the tax loan when it isdetermined that the taxpayer may be provided the tax loan, the tax loanbeing provided in periodic installments; monitoring, using the models,the taxpayer's additional withholding data on a periodic basis; andadjusting, based on the output of the models, the amount of the periodicloan payments to the taxpayer.
 2. The method of claim 1, furthercomprising: training, by the server, the regression model for predictingtax refund amounts using the supervised learning framework by:retrieving data, via a second server, from the community databasecomprising data associated with the taxpayer and data for a plurality ofadditional taxpayers comprising the community of taxpayers; labeling theretrieved data from the community database, based on a prior year's taxrefund of the taxpayers within the community, with a first set oflabels, wherein the labels comprise attributes relating to tax refundsincluding one or more of minimum refund amount, maximum refund amount,median refund amount, mean refund amount, standard error of the refundmean, industry code, credit score, or number of jobs; and passing datalabeled with the first set of labels through the regression model togenerate the trained regression model.
 3. The method of claim 2, furthercomprising: training, by the server, the classification model forpredicting tax loan risk using the supervised learning framework by:labeling the retrieved data from the community database, based on risksassociated with the taxpayers within the community, with a second set oflabels, wherein the second set of labels comprises at least the firstset of labels; and passing data labeled with the second set of labelsthrough the classification model to generate the trained classificationmodel.
 4. The method of claim 1, further comprising: training, by theserver, the second regression model for tax withholding analysis usingthe supervised learning framework by: labeling the retrieved data fromthe communicate database, based on job attributes and payroll dataassociated with the taxpayers within the community, with a third set oflabels; and passing data labeled with the third set of labels throughthe second regression model to generate a second trained regressionmodel for predicting withholding amounts.
 5. The method of claim 1,wherein the periodic installments comprise monthly installments.
 6. Themethod of claim 5, wherein adjusting the amount of the periodic loanpayments to the taxpayer further comprises: receiving updated dataassociated with the taxpayer; passing the updated data associated withthe taxpayer through the trained regression model to determine anupdated predicted tax refund; passing the updated data associated withthe taxpayer through the trained classification model to determine anupdated risk of providing the tax loan to the taxpayer; and adjusting anamount of the monthly installments based on the updated predicted taxrefund and the updated risk of providing the tax loan to the taxpayer.7. The method of claim 6, wherein the updated data associated with thetaxpayer is received from one or more of financial services associatedwith the taxpayer or data from a survey that was completed by thetaxpayer.
 8. The method of claim 1, further comprising: using thepredictive tax withholding amount to increase the taxpayer's pay periodincome for a paycheck.
 9. The method of claim 8, wherein the secondtrained regression model is trained retroactively by: labelingattributes of community tax data after a completion of a tax year; andpassing the labeled attributes of the community tax data through thesecond regression model.
 10. A system for providing additional periodicincome to a taxpayer of a community of taxpayers who have used a taxpreparation service in the past to prepare tax returns for thetaxpayers, said system comprising a computing device configured to:receive, from a mobile computing device associated with the taxpayer, arequest for additional periodic income, wherein the income comprises atax refund loan paid by the tax preparation service to the taxpayer ininstallments, wherein the request is received via an interface on themobile computing device generated by an application executing on themobile computing device; input, in response to receiving the request, bya server, a first set of data associated with the taxpayer into atrained regression model for predicting a tax refund amount for thetaxpayer without the tax preparation service or the taxpayer preparing atax return, the trained regression model outputting the predicted taxrefund, wherein the trained regression model is trained using asupervised learning framework on data retrieved from a communitydatabase, the retrieved data being different from the first set of data;input, in response to receiving the request, by the server, the firstset of data associated with the taxpayer into a trained classificationmodel for determining a risk of providing a tax loan to the taxpayer,the trained classification model being of a different type of model thana type of the trained regression model, the trained classification modeloutputting the determined risk of providing the tax loan to thetaxpayer, wherein the trained classification model is trained using asupervised learning framework on the data retrieved form the communitydatabase; determine whether the taxpayer may be provided the tax refundloan in an amount of the predicted tax refund based on the determinedrisk, wherein the risk is based at least in part on whether the taxpayeruses the tax preparation service to prepare the current tax return;receive, from the mobile computing device via the interface, a requestby the taxpayer to opt in for providing additional withholding data tothe tax preparation service, wherein the additional withholding data isprovided by services related to but separate from the tax preparationservice and comprises payroll, credit score, and accounting dataassociated with the taxpayer; in response to the taxpayer opting in forproviding additional withholding data: input a first set of additionalwithholding data associated with a taxpayer into a second trainedregression machine learning model for predicting a tax withholdingamount for each pay period for the taxpayer, wherein the second trainedregression model is trained using a supervised learning framework on thedata retrieved form the community database; determine, based on theoutput of the models, that the taxpayer may be provided with a taxrefund loan in a defined amount to be paid to the taxpayer on aninstallment basis; in response to determining that the taxpayer may beprovided with the tax loan, present a selection graphical object, in auser interface, for the taxpayer to accept the loan; in response toreceiving a selection of the taxpayer accepting the loan, generate anadditional periodic income for the taxpayer by: providing the taxpayerwith the tax loan if it is determined that the taxpayer may be providedthe tax loan, the tax loan being provided in periodic installments;monitoring, using the models, the taxpayer's additional withholding dataon a periodic basis; and adjusting, based on the output of the models,the amount of the periodic loan payments to the taxpayer.
 11. The systemof claim 10, wherein the computing device is further configured to:train the regression model for predicting tax refund amounts using thesupervised learning framework by: retrieving data, via a secondcomputing device, from the community database comprising data associatedwith the taxpayer and data for a plurality of additional taxpayerscomprising the community of taxpayers; labeling the retrieved data fromthe community database, based on a prior year's tax refund of thetaxpayers within the community, with a first set of labels, wherein thelabels comprise attributes relating to tax refunds including one or moreof minimum refund amount, maximum refund amount, median refund amount,mean refund amount, standard error of the refund mean, industry code,credit score, or number of jobs; and passing data labeled with the firstset of labels through the regression model to generate the trainedregression model.
 12. The system of claim 11, wherein the computingdevice is further configured to: train the classification model forpredicting tax loan risk using the supervised learning framework by:labeling the retrieved data from the community database, based on risksassociated with the taxpayers within the community, with a second set oflabels, wherein the second set of labels comprises at least the firstset of labels; and passing data labeled with the second set of labelsthrough the classification model to generate the trained classificationmodel.
 13. The system of claim 10, wherein the computing device isfurther configured to: train the second regression model for taxwithholding analysis using the supervised learning framework by:labeling the retrieved data from the communicate database, based on jobattributes and payroll data associated with the taxpayers within thecommunity, with a third set of labels; and passing data labeled with thethird set of labels through the second regression model to generate asecond trained regression model for predicting withholding amounts. 14.The system of claim 10, wherein the periodic installments comprisemonthly installments.
 15. The system of claim 14, wherein to adjust theamount of the periodic loan payments to the taxpayer, the computingdevice is further configured to: receive updated data associated withthe taxpayer; pass the updated data associated with the taxpayer throughthe trained regression model to determine an updated predicted taxrefund; pass the updated data associated with the taxpayer through thetrained classification model to determine an updated risk of providingthe tax loan to the taxpayer; and adjust an amount of the monthlyinstallments based on the updated predicted tax refund and the updatedrisk of providing the tax loan to the taxpayer.
 16. The system of claim15, wherein the updated data associated with the taxpayer is receivedfrom one or more of financial services associated with the taxpayer ordata from a survey that was completed by the taxpayer.
 17. The system ofclaim 10, wherein the computing device is further configured to: use thepredictive tax withholding amount to increase the taxpayer's pay periodincome for a paycheck.
 18. The system of claim 17, wherein the computingdevice is further configured to retroactively train the second trainedregression model by: labeling attributes of community tax data after acompletion of a tax year; and passing the labeled attributes of thecommunity tax data through the second regression model.
 19. A computerimplemented method for providing additional periodic income to ataxpayer, the method provided by a tax preparation service to acommunity of taxpayers who have used the tax preparation service in thepast to prepare tax returns for the taxpayers, said method comprising:receiving, from a mobile computing device associated with the taxpayer,a request for additional periodic income, wherein the income comprises atax refund loan paid by the tax preparation service to the taxpayer ininstallments, wherein the request is received via an interface on themobile computing device generated by an application executing on themobile computing device; inputting, in response to receiving therequest, by a first server, a first set of data associated with thetaxpayer into a trained regression model for predicting a tax refundamount for the taxpayer without the tax preparation service or thetaxpayer preparing a tax return, the trained regression model outputtingthe predicted tax refund, wherein the trained regression model istrained by the first server using a supervised learning framework ondata retrieved via a second server from a community database, theretrieved data being different from the first set of data; inputting, inresponse to receiving the request, by the first server, the first set ofdata associated with the taxpayer into a trained classification modelfor determining a risk of providing a tax loan to the taxpayer, thetrained classification model being of a different type of model than atype of the trained regression model, the trained classification modeloutputting the determined risk of providing the tax loan to thetaxpayer, wherein the trained classification model is trained by thefirst server using a supervised learning framework on the data retrievedvia the second server from the community database; determining whetherthe taxpayer may be provided the tax refund loan in an amount of thepredicted tax refund based on the determined risk, wherein the risk isbased at least in part on whether the taxpayer uses the tax preparationservice to prepare the current tax return; receiving, from the mobilecomputing device via the interface, by the first server, a request bythe taxpayer to opt in for providing additional withholding data to thetax preparation service, wherein the additional withholding data isprovided by services related to but separate from the tax preparationservice and comprises payroll, credit score, and accounting dataassociated with the taxpayer; in response to the taxpayer opting in forproviding the additional withholding data: inputting, by the firstserver, a first set of additional withholding data associated with thetaxpayer into a second trained regression machine learning model forpredicting a tax withholding amount for each pay period for thetaxpayer, wherein the second trained regression model is trained using asupervised learning framework by the first server on the data retrievedvia the second server from the community database; determining, based onthe output of the models, that the taxpayer may be provided with a taxrefund loan in a defined amount to be paid to the taxpayer on aninstallment basis; in response to determining that the taxpayer may beprovided with the tax loan, presenting a selection graphical object, ina user interface, for the taxpayer to accept the loan; in response toreceiving a selection of the taxpayer accepting the loan, generating anadditional periodic income for the taxpayer by: providing the taxpayerwith the tax loan when it is determined that the taxpayer may beprovided the tax loan, the tax loan being provided in periodicinstallments; monitoring, using the models, the taxpayer's additionalwithholding data on a periodic basis; and adjusting, based on the outputof the models, the amount of the periodic loan payments to the taxpayer.20. The method of claim 19, wherein adjusting the amount of the periodicloan payments to the taxpayer further comprises: receiving updated dataassociated with the taxpayer; passing the updated data associated withthe taxpayer through the trained regression model to determine anupdated predicted tax refund; passing the updated data associated withthe taxpayer through the trained classification model to determine anupdated risk of providing the tax loan to the taxpayer; and adjusting anamount of the periodic installments based on the updated predicted taxrefund and the updated risk of providing the tax loan to the taxpayer.